Worker Allocation on Engine Yard Cloud

Workers are the processes that allow your application to respond to incoming web requests. Regardless of the application server stack you select, it provides one or more workers per instance to run your application.

Standard worker allocations based on instance ECUs and memory configuration

For Passenger, Unicorn, Thin or Mongrel, worker processes are allocated using an algorithm that takes into consideration ECUs and memory available to the instance to determine the number of workers to allocate, as shown in the table below.

As of the May 21st, 2013 stack release, we modified the algorithm that generates the worker counts to better use the available resources; the result is increased numbers of worker processes for most instance sizes.

Instance Type

Memory (MB)

Swap (MB)

ECUs

Pool Size

Original

New

General Purpose (M1) Small

1700

895

1

3

3

General Purpose (M1) Medium

3840

1343

2

7

7

Compute Optimized (C1) Medium

1740

1343

5

6

6

General Purpose (M1) Large

7680

8192

4

6

8

General Purpose (M1) Extra Large

15360

8192

8

12

16

Compute Optimized (C1) Extra Large

6941

8192

20

24

29

Memory Optimized (M2) Extra Large

17500

8192

6.5

8

13

Memory Optimized (M2) Double Extra Large

35000

8192

13

8

26

Memory Optimized (M2) Quadruple Extra Large

70000

8192

26

24

52

Storage Optimized (Hi1) Quadruple Extra Large

62000

8192

35

70

70

General Purpose (T2) Micro

998

8192

3.25

-

6

General Purpose (T2) Small

2004

8192

3.25

-

6

General Purpose (T2) Medium

3697

8192

6.5

-

13

General Purpose (T2) Large

8192

8192

6.5

-

13

General Purpose (M3) Medium

3750

8192

3

-

6

General Purpose (M3) Large

7444

8192

6.5

-

13

General Purpose (M3) Extra Large

14960

8192

13

-

26

General Purpose (M3) Double Extra Large

30000

8192

26

-

52

General Purpose (M4) Large

7723

8192

6.5

-

13

General Purpose (M4) Extra Large

15775

8192

13

-

26

General Purpose (M4) Double Extra Large

31879

8192

26

-

52

General Purpose (M4) Quadruple Extra Large

64082

8192

53.5

-

100

General Purpose (M4) Decuple (10x) Extra Large

160561

8192

124.5

-

100

Compute Optimized (C3) Large

3750

8192

7

-

14

Compute Optimized (C3) Extra Large

7441

8192

14

-

28

Compute Optimized (C3) Double Extra Large

14961

8192

28

-

56

Compute Optimized (C3) Quadruple Extra Large

30058

8192

55

-

100

Compute Optimized (C3) Octuple Extra Large

60066

8192

108

-

100

Compute Optimized (C4) Large

3765

8192

8

-

13

Compute Optimized (C4) Extra Large

7219

8192

16

-

26

Compute Optimized (C4) Double Extra Large

14767

8192

31

-

52

Compute Optimized (C4) Quadruple Extra Large

29858

8192

62

-

100

Compute Optimized (C4) Octuple Extra Large

60050

8192

132

-

100

Memory Optimized (R3) Large

15026

8192

6.5

-

13

Memory Optimized (R3) Extra Large

30383

8192

13

-

26

Memory Optimized (R3) Double Extra Large

61096

8192

26

-

52

Memory Optimized (R3) Quadruple Extra Large

122519

8192

52

-

100

Memory Optimized (R3) Octuple Extra Large

245373

8192

104

-

100

About ECUs and vCPUs

We use "vCPUs" as a generic label for virtual CPU cores (and in some cases hyperthreads) available to your instance. Amazon (IaaS provider for Engine Yard Cloud) uses the term ECU, or Elastic Compute Units, to convey the relative compute power available on each instance type. This value is different than the virtual CPUs reported by the operating system, such as in top or /proc/cpuinfo, but better allows you to determine the amount of effective compute resources you need. According to Amazon, the definition of ECU is:

For the purpose of this feature, you can consider vCPUs to be synonymous with ECUs.

Tune for your application's needs

The values in the table (above) are based on generic parameters of an idealized application. However, if your app falls outside these parameters, you should consider tuning your environment, to either:

Reduce the worker counts to improve responsiveness because your app requires more resources.

Increase available workers for a lightweight app with high requests per minute.

Tune your app using parameters

You can tune your app by setting parameters on your environment. The sections below explain the various parameters that can be tuned. Once you know what values you want tuned, you need to open a support ticket to have them put in effect.

Important: In the case of General Purpose (M1) Medium and Compute Optimized (C1) Medium instances, there is an override in place to ensure that pool size from the values provided in the original algorithm are not reduced. If you tune one or more parameters, these overrides are removed and the generated values (based on your updated parameters) will be used instead.

pool_size

This is the most direct way of modifying your pool size. By specifying pool_size, you can set it regardless of resource availability. This is recommended only if you are fully aware of your resource needs and don't plan on changing your instance size in the future.

min_pool_size

Slightly less forceful, the min_pool_size parameter allows you to ensure your application always has the minimum number of workers available. Useful if using the smaller instance types and you don't mind overloading the resources — be careful though, overloaded instances may result in underperforming or even hung instances, potentially triggering a takeover.

If you find yourself using this and having poor responsiveness or takeovers, please consider upgrading to a larger instance size.

Default: 3

max_pool_size

Likewise, the max_pool_size parameter allows you to ensure you don't have more workers than you need, regardless of resource availability. You may want to do this if your workers connect to external entities that cannot handle too many simultaneous connections.

Default: 100

worker_memory_size

This is the best and most accurate way of tuning your app based on memory usage. The standardized values above are based on an app in which each worker uses 250MB of memory, which is close the upper limit of usage we've seen across thousands of apps. If your app is significantly different than this, it is best to tune this value to get accurate resource usage calculations.

Default:250 (MB)

workers_per_ecu

Some apps are compute heavy, where others barely use any of the processor capacity. The standard configuration provides 2 workers per ECU; tuning this to your app's needs can improve its responsiveness.

Default: 2

swap_usage_percent

While it is not a good idea to go in to swap in most cases, it is a balancing act to provide the optimal number of workers. We have chosen a modest 25% usage of swap by the workers in order to provide a sufficient worker count without excessive paging. If you find that your app is spending a great deal of time in swap, you may want to change this value.

Note: It is usually due to CPU constraints rather than memory constraints that determines the workers available, but depending on how many workers you allow per CPU, this may not be the case. Under default settings, this value most significantly impacts the Compute Optimized (C1) Small, Medium, and Extra Large instances.

Default: 20 (%)

reserved_memory

If you are running background workers or other processes that eat into your memory resources, you may want to specify a chunk of memory to reserve. By default, this is set to a generous value that accommodates the OS and support processes.

Default: 1500 (MB)

reserved_memory_solo

Similar to reserved_memory but also takes into consideration the database that shared the instance in a single-instance (solo) environment.

Default: 2000 (MB)

db_vcpu_max and db_workers_per_vcpu

These settings are only applicable to single instance environments. By default, no consideration is taken to reserve ECU resources for the database, but if you have a database-heavy application, it would be beneficial to allocate some of the worker resources to the database instead. The number of workers represented by the db_workers_per_ecu parameter is removed from each ECU, up to the db_vcpu_max value.

Default: db_vcpu_max = 0; db_workers_per_ecu = 0.5

Planning Tool

Use the following form to determine how many workers your instances will have, and how much free memory and swap usage will be expected.

Settings

Calculated value

Edit the fields below. Leave blank for default value, which is shown in grey italics when not editing the field.

pool_size:

min_pool_size:

max_pool_size:

worker_memory_size:

MB

workers_per_ecu:

reserved_memory:

MB

reserved_memory_solo:

MB

swap_usage_percent:

%

db_vcpu_max:

db_workers_per_ecu:

Instance Type

Cluster Instances

Single Instance

Why are some of the swap allocations over 100 percent without tuning?

In the default configuration (no tuned fields, so using the values you see in gray), you might have noticed that some of the instances appear allocated above the swap limits of the instance (for example, 131.0%). This is because we wanted to ensure that the minimal worker counts from the previous allocation scheme were not reduced. In practice, this does not cause a problem for the vast majority of our users, because worker_memory_size and reserved_memory do not often reach the upper limit values in the default values above. However, if these values due cause you issues, this tool and the tuned values it produces will allow you to protect your app from being over-allocated.

If you have feedback or questions about this page, add a comment below. If you need help, submit a ticket with Engine Yard Support.

These aren't OS environment variables -- they are parameters we can set for each of your Engine Yard Cloud environments. Once you have values that you want to use, simply open a support ticket to request those values be set for one or more specific environments. Because these changes take effect in the main chef run, they cannot be applied using the custom cookbook recipes.

As with any change, its always best to try it out on a staging environment before you try it in production, but keep in mind that worker's memory footprint often grows over time and usage, so base your values on your production stats.

As explained in the Important paragraph in the " Tune your app using parameters" section and in the "Why are some of the swap allocations over 100 percent without tuning?" section, there are overrides in place when using the default values. These overrides are to ensure that our existing customers do not experience a sudden reduction of workers when they upgrade to the new allocation algorithm; for a significant majority of our users, this does not impact their application. However, if your app is close to the limits used by the default values in the algorithm, you may experience poor performance.

Because you can be more accurate with your resource needs when specifying them yourself, we remove these overrides when using custom values so that the worker counts better reflect your intended usage.